Intrinsic
connectional architecture of the brain is a crucial element in understanding
the governing principle of brain organization. To date, enormous effort has
been focused on addressing this issue in humans by resting-state functional
magnetic resonance imaging (rsfMRI). rsfMRI noninvasively
measures functional connectivity without external stimulation, based on
spontaneous low frequency fluctuations of the blood oxygenation level dependent
(BOLD) signal. Using this technique, resting-state functional connectivity
(RSFC) has been consistently revealed in multiple networks of the human brain,
and has been shown to be altered by various factors such as sleep, anesthesia,
as well as neurological and psychiatric disorders.

Although much
progress has been made in understanding connectional architecture of the human
brain, this research area is significantly underexplored in animals, perhaps
because of confounding effects of anesthetic agents used in most animal experiments
on rsfMRI. To bridge this gap, we have systematically investigated the
intrinsic connectional architecture by using a previously established
awake-animal imaging model. The following results have been published recently (Liang
et al., 2011).

First, group
independent component analysis (ICA) was applied to the rsfMRI data to extract
elementary functional clusters of the brain. Most ICA
components identified were located in specific anatomical regions, including
bilateral caudate putamen, hippocampus, somatosensory cortex, thalamus and
other cortical and subcortical regions. Therefore, this step provided a global
layout of functional clusters in the awake rat brain. To our knowledge, this is
currently the first study utilizing group ICA to study RSFC in rat brain, with
only one previous rat ICA study at individual level (Hutchison et al.,
2010).

Subsequently, the
connectional relationships between these clusters were evaluated by partial
correlation analysis. Partial correlation coefficients between time courses of
any two ICA components were calculated, controlling the rest of ICA components.
Compared to widely used Pearson’s correlation in RSFC analysis, partial
correlation could eliminate a large portion of connectivity that is mediated by
other components, leaving largely direct connectivity.

The partial
correlation coefficients matrices were then used to construct whole-brain functional
neural network. Graph theoretical analysis was carried out for this network to
examine two network characteristics: small-worldness and modular structure. This
global network exhibited typical features of small-worldness, characterized by
relatively high clustering coefficient and almost the same average shortest
path length compared to random networks. Thus this result suggests that
small-worldness is conserved in awake rat functional neural network, similar to
results seen in human studies.

In addition, this
network also exhibited strong community structure as seen in many biological
and social networks. Modularity based community detection algorithm revealed
significantly higher modularity (Q) value (Q =0.414) compared to
random networks (p value<0.01), suggesting a significant modular structure.
The whole-brain functional network was first partitioned into three modules. The
first module predominantly extended across the cortical ribbon, indicating a
strong intercortical communication across the cortex. The second module
highlighted the olfactory pathway and its interaction with prefrontal cortex
(PFC), and the integration of other sensory input, cognitive processing, and
output in cortical and subcortical regions. Regions in the third module,
including PFC, insular cortex, hypothalamus, and amygdala, are all key
components subserving emotional and autonomic regulations. To address
degeneracy issue of the modularity function, distributions of Q values
and community structures were obtained for 20 repetitions. The result showed
that the later two of the three modules previously identified were highly
consistent across all repetitions with little variation, whereas the community
structure of cortical regions was further divided into two sub-modules in the
majority of repetitions.

Overall, the results
of this work provided a functional atlas of intrinsic connectional architecture
of the rat brain at both intraregional and interregional levels. More
importantly, the current work revealed that functional networks in rats are
organized in a nontrivial manner and conserve fundamental topological
properties that are also seen in the human brain. Given the high psychopathological
relevance of network organization of the brain, this study demonstrated the
feasibility of studying mechanisms and therapies of multiple neurological and
psychiatric diseases through translational research.